• Small (1400 students) undergraduate only liberal arts college
  • Smaller class sizes
  • Very motivated and quirky student body
  • Socially progressive but academically conservative
• Established in 1908 because founder
  • Didn't care for sports
  • Despised social clubs
• Huge de-emphasis on grades
• High degree of interaction between departments

• Not a vocational school
• High proportion of students who continue to do a PhD
• All students must:
  • take a junior qualifying exam
  • write a senior thesis
  • take freshman humanities class: HUM 110

• Statistics within the math department
• Classes:
  • Intro stats
  • Year-long junior level prob & math stats sequence
  • Applied/methods classes in other departments
• New class: MATH241 Case Studies in Statistical Analysis
• 

Concern from the stats community

• Former ASA president Marie Davidson "Aren't we data science?"
• Former IMS president Bin Yu "Let us own data science"
• UC Davis Prof. Norman Matloff "Statistics Losing Ground To CS, Losing Image Among Students"

Former Googler Rachel Schutt taught a similar Data Science class course at Columbia University.

Prereqs

Only intro stats and some exposure to R

Syllabus

• 5 biweekly mini-reports
  • submitted in R Markdown: reproducible research
  • large amount of feedback from me
• Term project: both report and 20 min oral
• In-class participation

18 students, mostly juniors and seniors.

ASA's GAISE Reports

• Use real data.
• Stress conceptual understanding, rather than mere knowledge of procedures.
• Foster active learning in the classroom.
• Use technology for developing conceptual understanding and analyzing data.

• Messy data, from potentially disparate sources
• Bottom-up: Let questions/data motivate the statistical methodology, rather than vice-versa
• Discussions in class
• Lean on R heavily
• Focus on the entire analysis pipeline: article in Nature

• Key: Forget Base R
• How? The Hadleyverse.
• In particular
  • dplyr package for data wrangling/manipulation
  • ggplot2 package for data visualization

Data manipulation via the following verbs on tidy data:

1. filter: keep observations matching criteria
2. summarise: reduce many values to one
3. mutate: create new variables from existing ones
4. arrange: reorder rows
5. select: pick columns by name
6. join: join two data sets
7. group_by: group subsets of observations together

• Moral: No matrix indexing or for loops

A statistical graphic consists of a mapping of data variables to aesthetic attributes of geometric objects that we can observe.

ggplot2 allows us to construct graphics in a modular fashion by specifying these components.

Domestic flights leaving Houston airport (IAH) in 2011. Four data sets:

• flights: info on all 227,496 flights
• weather: hourly weather info
• planes: information on all 2853 airplanes
• airports: information on all 3376 destination airports

• Sample of 10% of San Francisco OkCupid users in June 2012 (\(n=5995\))
• 40.2% of the sample was female
  
• Use logistic regression to predict gender
• Overfitting, out-of-sample prediction, cross-validation

Best predictors have distinct differences (in gender) in large segments of the population.

All 222,540 songs played on the Reed poolhall room jukebox from 2003-2009.

quandl.com has a great R interface

• Portland Crime
• Insect Abundance

• Econ junior: used to love STATA, now building a Shiny app for his senior thesis
• Biology major: most useful class he's taken at Reed
• Biology major: if she took this class earlier, it would have convinced her to be a Math major
• Seniors: incorporated such tools in their thesis

• Statistical topics: More on dependent data, missing data, causal inference, and some machine learning
• Databases/SQL
• Ask better questions
• Flipped-classroom
  • Lab exercises at home
  • Problem solving/debugging and discussion in class

• You hear this a lot:
  • Statistician: Hi, my name is X. I'm a statistician.
  • Non-statistician: Statistics? I hated that class.
• You'll never hear this:
  • Statistician: Hi, my name is X. My work involves a lot of data visualization.
  • Non-statistician: Data visualization? I hate that stuff.

• Data visualization is a backdoor way to get students interested in statistics.
• Prez from Season 4 of "The Wire":
• Students loved ggplot, maps, and Shiny apps

This is the only stats class many will take.

• Get them looking at, manipulating, and visualizing data quick
• Better integration of lectures and labs
• Randomization based methods. Tim Hesterberg's paper What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum

• Point-and-click vs command line.
  
• Thinking algorithmically
• Debugging: help files and Google
• Not easy: like learning a language

• A statistics class focused on the data first, methods second
• Rich Majerus wrote "Why should students at a small liberal arts college learn R?"
  • Learned R using dplyr and ggplot, not base R.
  • New tools like Datacamp are increasing the ratio: \[\frac{\mbox{Payoff from learning R}}{\mbox{Startup costs}}\]
• Data visualization as a "backdoor entrance" for statistics
• Developing skills and intuition takes time. At Reed classes are small: attention and feedback
• Interactivity boosts student interest

Presentation on 2011/06/27 given by Dierdre and Amir:

• Look at your data ASAP.
• Don't thrash
  • Do your due diligence, but don't overdo.
  • Seek expert advice.
• "You actually don’t know what you are doing until after you have done it."
• Do the most braindead thing first, take it end to end, then iterate/improve.
• Fight perfectionistism: think of the marginal return of your efforts.

Ressources

• Slides: RPubs.com/rudeboybert
• Code for slides and all material for this class github.com/rudeboybert/MATH241